An Efficient Clustering Method in Unlabeled Data Sets using KMBA Algorithm
Abstract
Cluster analysis is one of the primary data analysis
methods and K-means algorithm is well known for its efficiency in
clustering large data sets. The K-means (KM) algorithm is one of the
popular unsupervised learning clustering algorithms for cluster the
large datasets but it is sensitive to the selection of initial cluster
centroid, and selection of K value is an issue also sometimes it is hard
to predict before the number of clusters that would be there in data.
There are inefficient and universal methods for the selection of K
value, till now we selected that as random value. In this paper, we
propose a new metaheuristic method KMBA, the KM and Bat
Algorithm (BA) based on the echolocation behavior of bats to identify
the initial values for overcome the KM issues. The algorithm does not
require the user to give in advance the number of clusters and cluster
centre, it resolves the K-means (KM) cluster problem. This method
finds the cluster centre which is generated by using the BA, and then it
forms the cluster by using the KM. The combination of both KM and
BA provides an efficient clustering and achieves higher efficiency.
These clusters are formed by the minimal computational resources and
time. The experimental result shows that proposed algorithm is better
than the existing algorithms.
Keywords
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A.M.Fahim, A.M.Salem, F.A.Torkey and M.A.Ramadan, “An efficient
enhanced K-means clustering algorithm”, Journal of Zhejiang University
Science A, pp 16261-633, May 2006.
Dubes R, “Cluster analysis and related issue”. In: Chen C, Pau L, Wang P
(eds) Handbook of pattern recognition and computer vision, River Edge,
NY: World Science Publishing Company, pp 3-32, 1993.
Bradley P, Fayyad U, “Refining initial points for K-means clustering”
International conference on machine learning (ICML-98) pp 91-99, 1998.
M. N. Vrahatis, B. Boutsinas, P. Alevizos and G. Pavlides, “The New
K-Windows Algorithm for Improving the K-Means Clustering
Algorithm” Journal of complexity, Vol.18, pp 375-391, Mar 2002.
Xin-She Yang, “A New Metaheuristic Bat-Inspired Algorithm”
Department of Engineering, University of Cambridge, April 2010.
Kennedy, J. and Eberhart, R., “Particle swarm optimization”, Proc. IEEE
Int. Conf. Neural Networks. Perth, Australia, Nov/Dec 1995.
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P., “Optimization by
simulated annealing” Science, 220, 671-680, May 1983.
Jinxin Dong, Minyong Qi “A New Algorithm for Clustering Based on
Particle Swarm Optimization and K-means”, International Conference on
Artificial Intelligence and Computational Intelligence, Nov 2009.
UCI Repository of Machine Learning Databases,
http://www.ics.uci.edu/~mlearn/MLRepository. html.
S. Sujatha , A. Shanthi Sona , “New Fast K-Means Clustering Algorithm
using Modified Centroid Selection Method” , International Journal of
Engineering Research & Technology (IJERT),pp 1 – 9, Vol. 2 Issue 2,
February- 2013.
Jieming Wu; Wenhu Yu; “Optimization and Improvement Based on
K-Means Cluster Algorithm”, Second International Symposium on
Knowledge Acquisition and Modeling (KAM '09), Vol. 3, Pp. 335 – 339,
Aristidis Likas, Nikos Vlassis, and Jakob J. Verbeek, “The global
k-means clustering algorithm,” The Journal of Pattern Recognition
society, Elsevier, vol. 36, no. 2, pp. 451-461, 2003.
D. Napoleon & P. Ganga lakshmi, “An Efficient K-Means Clustering
Algorithm for Reducing Time Complexity using Uniform Distribution
Data Points”, IEEE, 2010.
K.A.Abdul Nazeer, S.D.Madhu Kumar, M.P.Sebastian “Enhancing the
k-means clustering algorithm by using a O(n logn) heuristic method for
finding better initial centroids”, Second International Conference on
Emerging Applications of Information Technology 2011.
Mehdi Neshat, Shima Farshchian Yazdi,Daneyal Yazdani and Mehdi
Sargolzaei, “A New Cooperative Algorithm Based on PSO and K-Means
for Data Clustering”, Journal of Computer Science , pp 188-194, 2012.
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